This article explains the concept of LangChain retrievers, compares them with vector stores, and provides step‑by‑step code examples for MultiQueryRetriever and EnsembleRetriever, showing how to boost recall and combine semantic and keyword search for robust RAG pipelines.
This guide walks through the complete process of creating a RAG‑powered question‑answering bot using LangChain, Streamlit, and vector‑store embeddings, covering theory, architecture, data loading, chunking, vector indexing, retrieval, LLM integration, and full code implementation with practical examples.
LangChain4j streamlines the integration of large language models into Java applications by offering a standardized API, extensive support for over a dozen LLM providers and vector stores, a rich toolbox for RAG, chat memory, and tool calling, plus two abstraction layers that cater to both low‑level control and high‑level convenience.
This article explains how to build an offline knowledge base for IT operations by combining large language models with Retrieval‑Augmented Generation (RAG) using LangChain, covering document loading, chunking, embedding, vector storage, and query‑time retrieval with concrete code examples.
This article introduces LangChain—a Python framework for building LLM‑driven applications—explains its core components such as models, indexes, chains, memory, and agents, and provides practical code examples for document summarization, retrieval‑augmented QA, and future development directions.
This article explains what LangChain is, outlines its core components such as Models, Indexes, Chains, Memory and Agents, and demonstrates how to build LLM‑driven applications with detailed Python code snippets, visual diagrams, and future development suggestions.
This article introduces LangChain, explains why it’s useful for building applications with large language models, walks through installation, API‑key setup, model and embedding selection, prompt engineering, chaining, memory, agents, and vector‑store indexing, and provides runnable Python code examples throughout.
